The present invention relates to the field of data mining of large complex networks of information nodes.
Mining large complex networks of information nodes is a difficult task due to the sheer volume of data. A visual interaction with the network can be used to aid in the mining process. However, visual mining of large networks is a complicated and expensive process. In the study of complex networks, a network is said to have a community structure if the nodes of the network can be easily grouped into sets of nodes such that each set of nodes is densely connected internally. Community detection algorithms can be applied to these networks to find the community structure. However, the results of these algorithms may be difficult for an end-user to understand, especially when the dataset considered involves more than a few dozens of nodes. Also, community detection algorithms are costly mathematical processes where users are not allowed to interfere. If the user wants to change any parameter or change the algorithm, the user needs to re-run the entire algorithm all over again.